Electric vehicle charging identification method and device based on overshoot detection and current characteristic analysis
By using overshoot detection and current characteristic analysis, the accuracy and stability issues of electric vehicle charging identification are solved, enabling automatic identification of electric vehicle charging in complex power environments. This method is suitable for electric vehicle charging management and safety monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-25
- Publication Date
- 2026-03-31
AI Technical Summary
Existing electric vehicle charging management methods rely on manual inspections and simple power threshold judgments, which make it difficult to accurately identify electric vehicle charging behavior in complex power environments. This leads to problems such as misjudgment, missed judgment, and untimely detection, affecting building power safety.
A method based on overshoot detection and current characteristic analysis is adopted to identify electric vehicle charging behavior through reference current correction, overshoot detection, event classification and differential processing. This includes matching household appliance waveforms for short-term overshoot events and analyzing the power consumption stability characteristics of long-term overshoot events, and establishing a household appliance waveform database for accurate identification.
It achieves accurate identification of electric vehicle charging behavior, reduces the probability of false judgment, improves the stability and anti-interference ability of identification, and is suitable for safety management in residential communities and building scenarios. It has low cost and wide applicability.
Smart Images
Figure CN121770084A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of electric vehicle charging safety technology, and in particular relates to an electric vehicle charging identification method and device based on overcharge detection and current characteristic analysis. Background Technology
[0002] In recent years, the number of electric vehicles such as electric bicycles and electric motorcycles in residential communities and buildings has increased rapidly, becoming an important tool for residents' commuting and delivery. However, the resulting problems of parking electric vehicles in buildings and privately installing power cords for charging inside buildings or in corridors have become increasingly common, posing significant hidden dangers to fire accidents and electrical safety management in residential communities. On the one hand, electric vehicle batteries are prone to overcharging, short circuits, and overheating during charging, especially when the battery is aging, has been illegally modified, or is using an uncertified charger, which can easily lead to thermal runaway, causing fires or even explosions. On the other hand, the charging power of electric vehicles is usually higher than that of ordinary small household appliances. Prolonged connection to non-designed power locations such as corridors and stairwells may cause the branch current to approach or exceed the design capacity, leading to electrical faults such as overheating, aging of the wiring, and insulation breakdown, affecting the power supply safety and stability of the entire building.
[0003] From a spatial and management perspective, the parking and charging of electric vehicles in buildings often leads to problems such as haphazardly installed electrical wires and the long-term occupation of parking spaces in public areas like stairwells and corridors. This not only affects pedestrian traffic and emergency evacuation but also increases safety risks such as moisture and wear on electrical cables. To mitigate these hazards, many residential communities and buildings have explicitly prohibited electric vehicles from entering buildings and charging inside through property management regulations and fire safety systems. However, in practice, existing management methods rely heavily on manual inspections and simple power outages, which suffer from problems such as untimely inspections, difficulty in obtaining evidence, and difficulty in timely and accurate identification of specific violations. Some technical solutions based on electricity consumption data often use simple current thresholds or coarse-grained power judgments, making it difficult to effectively distinguish between electric vehicle charging and ordinary household appliance electricity consumption, and prone to misjudgment and omission in scenarios with multiple devices using electricity simultaneously.
[0004] However, current management methods for electric vehicle charging still rely mainly on manual inspections and simple power threshold judgments. These methods are easily affected by normal electricity consumption from household appliances, leading to problems such as misjudgments, missed judgments, and delayed detection, making them unsuitable for the current safety management needs of building environments. Therefore, it is necessary to propose a new electric vehicle charging identification method and device to more effectively and automatically identify and manage electric vehicle charging behavior based on existing power facilities. Summary of the Invention
[0005] The purpose of this invention is to provide an electric vehicle charging identification method and device based on overcharge detection and current characteristic analysis. This method can automatically identify electric vehicle charging behavior based on existing power facilities, improve the timeliness and accuracy of detecting illegal charging of electric vehicles, thereby reducing the safety risks of building electricity use and meeting the safety management needs of residential communities and building scenarios.
[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for identifying electric vehicle charging based on overshoot detection and current characteristic analysis, comprising: Acquire real-time current data of the power circuit, perform reference current correction on the real-time current data to form a test current sequence updated in time order, and initialize the detection start position of overshoot detection. In the current test current sequence, overshoot detection is performed starting from the detection start position. When a current overshoot that meets the preset overshoot judgment condition is detected, the current overshoot and the corresponding current change range are determined as the current power consumption event. Based on the duration of the current change range corresponding to the current power consumption event, the current power consumption event is classified into a short-time overshoot event or a long-time overshoot event. If the current power consumption event is a short-term overshoot event, the power consumption segment corresponding to the short-term overshoot event is determined in the test current sequence, and the similarity of the power consumption segment with the complete current waveforms of multiple pre-configured household appliances is calculated. When there is a complete current waveform of a household appliance with a similarity greater than a preset similarity threshold, the current component corresponding to the complete current waveform of the household appliance is subtracted from the test current sequence to obtain an updated test current sequence. The detection start position is updated to the position after the current power consumption event, and the updated test current sequence is updated as the current test current sequence. Then, the overshoot detection is performed. If the current power consumption event is a long-duration overshoot event, perform power consumption stability feature analysis on the test current sequence; when the power consumption stability feature meets the preset stability condition in terms of duration, determine that there is electric vehicle charging behavior and output the corresponding identification result; when the power consumption stability feature does not meet the preset stability condition in terms of duration, update the detection start position to the position after the current power consumption event, and return to execute overshoot detection.
[0007] Furthermore, the steps for reference current correction include: The corrected current value is obtained by subtracting the real-time current value from the predetermined final reference correction value in the real-time current data. The steps for pre-determining the final reference correction value include: Acquire current value data within a preset time period, determine the lowest value range of current value data within the time period, and average the current values within the lowest value range to obtain the reference correction value; The final benchmark correction value is obtained by weighted averaging the benchmark correction values from multiple time periods.
[0008] Furthermore, the overshoot detection steps include: Discrete frequency domain analysis is performed on the current signal corresponding to the test current sequence to obtain the discrete spectrum of the current signal; Within the discrete frequency index interval corresponding to the preset high frequency range, the high frequency power value is calculated based on the discrete spectrum, where the high frequency power value is the sum of the squares of the amplitudes of each frequency component within the discrete frequency index interval; When the high-frequency power value is greater than the preset overshoot threshold, it is determined that there is current overshoot.
[0009] Further, the similarity calculation steps include: In the test current sequence, the position where the current overshoot occurs corresponding to the short-time overshoot event is taken as the starting position of the segment, and the current waveform data corresponding to the short-time overshoot event is extracted to form the power consumption segment. Based on the current waveform corresponding to the power consumption segment and the pre-configured complete current waveform of the household appliance, a corresponding waveform feature representation is constructed; The similarity between the waveform feature representation of the power consumption segment and the waveform feature representation of the complete current waveform of each household appliance is calculated to obtain the similarity value between the power consumption segment and the complete current waveform of each household appliance. The similarity value is compared with a preset similarity threshold. When the similarity value is greater than the preset similarity threshold, it is determined that the power segment matches the complete current waveform of the corresponding household appliance.
[0010] Furthermore, the step of subtracting the current component from the test current sequence includes: Using the overshoot start position corresponding to the short-time overshoot event as the alignment start point, the test current sequence is time-aligned with the complete current waveform of the household appliance; When the sampling frequency of the test current sequence is inconsistent with that of the complete current waveform of the household appliance, the current value corresponding to the complete current waveform of the household appliance is calculated at the sampling time of the test current sequence based on interpolation or fitting. At the corresponding sampling points after time alignment, the current value of the test current sequence is subtracted point by point from the corresponding current value of the complete current waveform of the household appliance to obtain the updated test current sequence.
[0011] Further steps in the power stability characteristic analysis include: Discrete frequency domain analysis is performed on the current signal corresponding to the test current sequence to obtain the discrete spectrum of the current signal; Within the discrete frequency index interval corresponding to the preset low frequency range, the low frequency power value is calculated based on the discrete spectrum, where the low frequency power value is the sum of the squares of the amplitudes of each frequency component within the discrete frequency index interval; When the low-frequency power value meets the preset stability threshold condition within a preset duration, the power consumption behavior corresponding to the test current sequence is determined to be electric vehicle charging behavior.
[0012] Furthermore, the complete current waveform of household appliances is the electrical waveform data of household appliances that has been pre-collected and stored in the waveform database. The complete current waveform of household appliances includes at least the current waveform of household appliances during the start-up phase, the stable operation phase, or the typical working cycle. The complete current waveforms of household appliances in the waveform database are updated using at least one of the following methods: The system collects the current of the corresponding circuit of household appliances during actual operation, and updates the collected current waveforms after periodic statistical and normalization processing; or it updates the complete current waveforms of household appliances in the waveform database by remote distribution.
[0013] In a second aspect, the present invention provides an electric vehicle charging identification device based on overshoot detection and current characteristic analysis, comprising: The data acquisition module is used to acquire real-time current data of the power circuit, perform reference current correction on the real-time current data to form a test current sequence updated in time order, and initialize the detection start position of overshoot detection. The overshoot detection module is used to perform overshoot detection from the detection start position in the current test current sequence. When a current overshoot that meets the preset overshoot judgment condition is detected, the current overshoot and the corresponding current change range are determined as the current power consumption event. The event classification module is used to classify the current power consumption event into a short-time overshoot event or a long-time overshoot event based on the duration of the current change range corresponding to the current power consumption event. The short-time overshoot module is used to determine the power consumption segment corresponding to the short-time overshoot event in the test current sequence if the current power consumption event is a short-time overshoot event. It then calculates the similarity between this power consumption segment and multiple pre-configured complete current waveforms of household appliances. When there is a complete current waveform of a household appliance with a similarity greater than a preset similarity threshold, the module subtracts the current component corresponding to the complete current waveform of the household appliance from the test current sequence to obtain an updated test current sequence. The module then updates the detection start position to the position after the current power consumption event, updates the updated test current sequence to the current test current sequence, and returns to execute the overshoot detection. The long-term overshoot module is used to perform power consumption stability feature analysis on the test current sequence if the current power consumption event is a long-term overshoot event. When the power consumption stability feature meets the preset stability condition in terms of duration, it determines that there is electric vehicle charging behavior and outputs the corresponding identification result. When the power consumption stability feature does not meet the preset stability condition in terms of duration, it updates the detection start position to the position after the current power consumption event and returns to execute overshoot detection.
[0014] In a third aspect, the present invention provides an electronic device including a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement an electric vehicle charging identification method based on overshoot detection and current characteristic analysis.
[0015] In a fourth aspect, the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements an electric vehicle charging identification method based on overshoot detection and current characteristic analysis.
[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: The electric vehicle charging identification device, electronic device, and computer-readable storage medium provided by this invention based on overshoot detection and current characteristic analysis also solve the problems mentioned in the background section.
[0017] 1. This invention proposes an electric vehicle charging identification method based on overshoot detection and current feature analysis. By performing benchmark current correction, overshoot detection, event classification, and differential processing on real-time current data of the power circuit, it achieves accurate identification of electric vehicle charging behavior. This method uses the test current sequence as a unified analysis object. First, it locates potential power consumption events through overshoot detection. Then, based on the duration of the current change interval, it classifies power consumption events into short-term and long-term overshoot events, and employs different identification paths for different types of events, thereby avoiding misidentification of ordinary household appliance start-stop behavior as electric vehicle charging behavior. Especially in short-term overshoot events, by matching the current waveform of household appliances and subtracting corresponding current components, it effectively eliminates the interference of multiple household appliances' superimposed power consumption on the identification results. In long-term overshoot events, reliable judgment of continuous electric vehicle charging behavior is achieved through power consumption stability feature analysis. This method requires no additional hardware modification and can be implemented based solely on existing power circuit current data. It has the advantages of low implementation cost, wide applicability, and clear identification logic, making it suitable for widespread application in scenarios such as residential power consumption monitoring, distribution-side load identification, and electric vehicle charging management.
[0018] 2. This invention further improves the stability and anti-interference capability of electric vehicle charging identification. First, by extracting the lowest current value interval within a preset time period and performing a weighted average, a stable final benchmark correction value is obtained. This effectively eliminates the influence of zero drift, current bias, and background load fluctuations on subsequent analysis, providing a reliable baseline for overshoot detection. Second, an overshoot detection method based on discrete frequency domain analysis is adopted. High-frequency power values are used to determine whether there are abrupt changes in current. Compared to simple time-domain threshold judgment, this method can more accurately distinguish between real power consumption events and random noise interference. In short-term overshoot event processing, by constructing feature representations of power consumption segments and the complete current waveform of household appliances and performing similarity calculations, accurate matching of the start-stop behavior of household appliances is achieved. Furthermore, based on time alignment and sampling frequency consistency processing, corresponding current components are deducted point by point. This gradually removes non-target power loads without disrupting the continuity of the overall test current sequence, providing a cleaner current data foundation for subsequent event identification.
[0019] 3. This invention further improves the reliability and long-term adaptability of electric vehicle charging behavior determination. In the identification of long-duration overshoot events, a low-frequency power value based on discrete frequency domain analysis is introduced as a characteristic of power stability. Combined with the duration condition, this effectively characterizes the typical characteristics of relatively stable current amplitude and concentrated spectral energy in the low-frequency range during electric vehicle charging, thus avoiding misjudging other loads with intermittent or periodic variations as electric vehicle charging behavior. Simultaneously, by establishing and maintaining a complete current waveform database of household appliances and supporting periodic statistical updates or remote updates based on data collected from actual power circuits, the waveform database can continuously adapt to different types of household appliances and their changing operating modes, enhancing the accuracy and timeliness of similarity matching and current deduction processes. Therefore, this invention not only has high accuracy in single identification but also maintains good identification performance continuously with changes in the power environment, possessing good engineering practical value and promising prospects for promotion. Attached Figure Description
[0020] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of an electric vehicle charging identification method based on overshoot detection and current characteristic analysis according to an embodiment of the present invention; Figure 2 This is an application flowchart of the electric vehicle charging identification method based on overshoot detection and current feature analysis according to an embodiment of the present invention; Figure 3 This is a structural block diagram of an electric vehicle charging identification device based on overshoot detection and current characteristic analysis according to an embodiment of the present invention; Figure 4 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation
[0021] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.
[0022] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.
[0023] Example 1 This embodiment provides an electric vehicle charging identification method based on overshoot detection and current characteristic analysis. It is suitable for continuously analyzing and judging real-time current data collected in power circuits to accurately identify the presence of electric vehicle charging behavior in scenarios where multiple electrical devices are operating simultaneously. This method uses the current signal as the core analysis object and, through processing steps such as reference current correction, overshoot event detection, event type differentiation, short-term overshoot stripping, and long-term overshoot stability determination, achieves effective identification of electric vehicle charging behavior and maintains high stability and accuracy in complex household or power distribution scenarios.
[0024] like Figure 1 and Figure 2 As shown, the electric vehicle charging identification method based on overshoot detection and current feature analysis provided by the present invention includes steps S1 to S5.
[0025] In step S1, real-time current data of the power circuit is acquired, and the real-time current data is corrected using a reference current to form a test current sequence updated in chronological order. The detection start position for overshoot detection is also initialized. Real-time current data can be acquired using a current acquisition device installed in the power circuit. This current acquisition device can be a smart meter, current transformer, or other equipment with current acquisition capabilities. Its sampling frequency can be set according to actual application requirements to ensure effective capture of changes in electricity consumption behavior.
[0026] Before performing reference current correction on real-time current data, a final reference correction value needs to be obtained. The final reference correction value is obtained as follows: First, current value data within a preset time period is acquired. This preset time period can be a low-load period, nighttime, or other statistically less loaded time interval within a day. Within this preset time period, the collected current value data is statistically analyzed to determine the lowest value range of the current data within the time period. The current values within the lowest value range are then averaged to obtain the reference correction value. Further, a weighted average is performed on the reference correction values obtained from multiple preset time periods to obtain the final reference correction value, calculated using the following formula:
[0027] in, This is the final baseline correction value after weighted averaging. For the first The baseline correction value obtained from the second acquisition For the first The weight corresponding to each collection. This represents the number of times the baseline correction value was collected. By performing a weighted average of the baseline correction values from multiple time periods, the impact of occasional outliers on the baseline correction results can be reduced, making the final baseline correction value more stable and reliable.
[0028] After obtaining the final reference correction value, the reference current correction in step S1 includes: subtracting the real-time current value from the real-time current data from the final reference correction value to obtain the corrected current value, thereby forming a test current sequence. Reference current correction effectively eliminates the influence of long-term background current or static load in the power circuit on subsequent analysis, making the test current sequence more prominently reflect the dynamic changes in power consumption behavior. Simultaneously, step S1 initializes the overshoot detection start position, indicating the initial position to begin overshoot detection in the test current sequence, ensuring the orderly progress of subsequent detection processes.
[0029] In step S2, overshoot detection is performed starting from the initial detection position in the current test current sequence. When a current overshoot that meets the preset overshoot judgment condition is detected, the current overshoot and the corresponding current change range are determined as the current power consumption event. Overshoot detection is achieved by performing discrete frequency domain analysis on the current signal corresponding to the test current sequence. First, the current signal corresponding to the test current sequence is subjected to discrete frequency domain transformation to obtain the discrete spectrum of the current signal. The calculation method is as follows:
[0030] in, For the first in the discrete frequency domain One frequency component, It is a discrete-time current signal. The total number of sampling points. For sampling point index, For frequency index, The imaginary unit is used. By performing discrete frequency domain analysis on current signals, transient changes that are not easily distinguishable in the time domain can be transformed into energy distribution characteristics of different frequency components in the frequency domain.
[0031] After obtaining the discrete spectrum, within the discrete frequency index interval corresponding to the preset high-frequency range, the high-frequency power value is calculated based on the discrete spectrum. The high-frequency power value is the sum of the squares of the amplitudes of each frequency component within the discrete frequency index interval, and its calculation method uses the following formula:
[0032] in, This is the high-frequency power value. This is the starting frequency for the high-frequency analysis range. The maximum frequency in the spectrum. This represents the amplitude of the corresponding frequency component. By calculating the high-frequency power value, the high-frequency components introduced into the current signal due to factors such as instantaneous startup and sudden load changes can be effectively characterized.
[0033] When the high-frequency power value is greater than the preset overshoot threshold, it is determined that there is a current overshoot. The determination condition is based on the following formula:
[0034] in, A preset overshoot threshold is set. Through the above determination, the location of significant transient changes in current can be accurately identified in the test current sequence, and the current overshoot and its corresponding current change range can be identified as the current power consumption event, providing a basis for subsequent event classification and identification processing.
[0035] In step S3, the current power consumption event is classified into a short-term overshoot event or a long-term overshoot event based on the duration of the current change range corresponding to the current consumption event. The duration can be obtained by statistically analyzing the number of sampling points or the actual time length covered by the current change range corresponding to the overshoot of the current power consumption event in the test current sequence. When the duration is less than a preset time threshold, the current power consumption event is determined to be a short-term overshoot event; when the duration is greater than or equal to the time threshold, the current power consumption event is determined to be a long-term overshoot event. Through the above distinction, short-term power consumption events caused by the instantaneous start-up and short-cycle operation of household appliances can be distinguished from power consumption events with continuous and stable load characteristics, laying the foundation for subsequent differentiated processing strategies for different types of events.
[0036] After completing steps S1 to S3, the system has obtained the test current sequence corrected for the reference current, and based on frequency domain analysis, identifies the current overshoot and its variation range corresponding to the current power consumption event. It also determines whether the current power consumption event is a short-term or long-term overshoot event. Based on this, subsequent identification processes are executed for different types of power consumption events.
[0037] When the current power consumption event is determined to be a short-time overshoot event in step S3, the process proceeds to step S4. In step S4, the power consumption segment corresponding to the short-time overshoot event is first determined in the test current sequence. Specifically, the moment when the current overshoot occurs corresponding to the short-time overshoot event is taken as the starting position of the segment. Starting from this starting position, current waveform data up to the moment the next current overshoot occurs is extracted. The extracted current waveform data constitutes a complete power consumption segment. By using the current waveform between two adjacent overshoots as the analysis object, it can be ensured that the power consumption segment covers a complete power consumption behavior process, thereby avoiding mixing multiple power consumption behaviors in the same analysis window and improving the accuracy of subsequent identification.
[0038] After obtaining the power consumption segment, the similarity of the power consumption segment with the complete current waveforms of multiple pre-configured household appliances is calculated. The complete current waveforms of household appliances are power consumption characteristic data that are pre-collected and stored in a waveform database. They can include the current waveforms formed by household appliances during the startup phase, stable operation phase, or typical working cycle, and are used to characterize the power consumption characteristics of different household appliances under normal operating conditions.
[0039] In this embodiment, the similarity calculation uses the cosine similarity formula, the mathematical expression of which is:
[0040] in, This represents a vector representation composed of the current waveforms of electrical segments. This represents a vector representation consisting of the complete current waveform of a household appliance. Representing vectors with vector The result of the dot product operation, Representing vectors The Euclidean norm, Representing vectors The Euclidean norm of the electrical circuit. The similarity value calculated by the above formula is used to reflect the degree of similarity in shape between the electrical segment and the complete current waveform of the corresponding household appliance. The larger the similarity value, the closer the current change trend of the two are.
[0041] The calculated similarity value is compared with a preset similarity threshold. When the similarity value is greater than the preset similarity threshold, it is determined that the power consumption segment matches the complete current waveform of the corresponding household appliance, thus it is considered that the short-term overshoot event is caused by the power consumption behavior of the household appliance.
[0042] After completing the matching determination of household appliances, current component subtraction processing is performed on the test current sequence to eliminate the impact of the identified household appliance power consumption behavior on subsequent power consumption event detection. Specifically, the overshoot start position corresponding to the short-term overshoot event is used as the time alignment start point, and the test current sequence is aligned with the complete current waveform of the household appliance on the time axis. After time alignment is completed, at the corresponding sampling time, the current value in the test current sequence is subtracted from the current value of the complete current waveform of the household appliance at that time to obtain the updated test current sequence.
[0043] When the sampling frequency of the test current sequence is inconsistent with the sampling frequency of the complete current waveform of the household appliance, the current value corresponding to the complete current waveform of the household appliance is first calculated at the sampling time of the test current sequence based on interpolation or fitting. Then, a point-by-point subtraction operation is performed to ensure the consistency and accuracy of the current component subtraction process in the time dimension. Through the above current component subtraction process, the identified electricity consumption behavior of the household appliance can be separated from the test current sequence without affecting other electricity consumption behavior characteristics.
[0044] After completing the identification of household appliances and the subtraction of current components corresponding to the short-term overshoot event, the detection start position of the overshoot detection is updated to the position after the current power consumption event ends, and the updated test current sequence is used as the current test current sequence. Then, the process returns to step S2 to continue detecting subsequent possible power consumption overshoot events.
[0045] When the current power consumption event is determined to be a long-duration overshoot event in step S3, the process proceeds to step S5 to perform power consumption stability characteristic analysis on the test current sequence. Long-duration overshoot events typically correspond to power consumption behaviors with a relatively long duration and relatively stable power changes, exhibiting characteristics of significant low-frequency energy proportion and small fluctuations in the frequency domain. Therefore, in step S5, discrete frequency domain analysis is performed on the current signal corresponding to the test current sequence to obtain the discrete spectrum of the current signal.
[0046] After obtaining the discrete spectrum, the low-frequency power value is calculated based on the discrete frequency index interval corresponding to the preset low-frequency range. This calculation involves summing the squares of the amplitudes of each frequency component within the low-frequency index interval. Analysis of the low-frequency power value can characterize the energy distribution of the test current sequence within the low-frequency range.
[0047] When the low-frequency power value continuously meets the preset stability threshold condition within a preset duration, it is determined that the power consumption behavior corresponding to the test current sequence has continuous and stable power consumption characteristics, thereby determining that electric vehicle charging behavior exists, and outputting the corresponding identification result. Conversely, when the low-frequency power value fails to meet the stability threshold condition within the preset duration, it is considered that the long-term overshoot event does not meet the characteristic requirements of electric vehicle charging behavior. At this time, the detection start position of the overshoot detection is updated to the position after the end of the current power consumption event, and the process returns to step S2 to continue detecting and analyzing subsequent power consumption events. Specifically, in step S5, the current signal corresponding to the test current sequence is subjected to discrete frequency domain analysis to obtain the discrete representation of the current signal in the frequency domain. Based on the discrete frequency domain analysis results, the low-frequency power value of the current signal is calculated within a preset low-frequency range, and the calculation formula is as follows:
[0048] in, To test the current sequence, discrete frequency domain analysis was performed at the frequency... The corresponding spectral components at that location, This is the starting frequency of the low-frequency range. This is the cutoff frequency in the low-frequency range. It represents the power value of the test current sequence in the low-frequency range, and is used to characterize the degree of energy concentration of electricity consumption behavior in the low-frequency range.
[0049] After obtaining the low-frequency power value, compare the low-frequency power value with a preset stability threshold. The comparisons are made, and the duration of the low-frequency power value over time is assessed. When the low-frequency power value meets... And this state occurs over a continuous period of time. It persists within, and its duration is... Not less than the preset duration threshold When the power consumption behavior corresponding to the test current sequence is determined to have continuous and stable low-frequency characteristics in terms of power characteristics, it is determined that there is electric vehicle charging behavior, and the corresponding recognition result is output.
[0050] Through the coordinated processing of steps S4 and S5, the short-term electricity consumption behavior of household appliances can be gradually separated in complex, multi-load superimposed power consumption environments, and the charging behavior of electric vehicles can be accurately identified based on the characteristics of power consumption stability, thereby improving the accuracy and robustness of the overall identification process.
[0051] This embodiment provides an electric vehicle charging identification method based on overshoot detection and current feature analysis. It performs joint analysis of the frequency domain and time-series characteristics of the current signal in the power consumption circuit. Through the coordinated processing of reference current correction, overshoot detection, event classification, and power consumption stability feature analysis, it can accurately identify electric vehicle charging behavior without additional hardware modifications. By introducing a similarity matching and current component subtraction mechanism based on the complete current waveform of household appliances in short-term overshoot events, it can effectively eliminate the interference of transient power consumption behaviors such as the start-stop of household appliances on the overall current sequence, allowing the subsequent identification process to focus on unexplained power consumption components, thereby significantly reducing the probability of misjudgment. In the identification of long-term overshoot events, by comprehensively judging low-frequency power characteristics and current stability conditions, it can accurately distinguish the continuous and stable power consumption characteristics of electric vehicle charging from other high-power load power consumption behaviors. The overall method has a clear process and closed-loop logic, and can operate stably in real-world scenarios with multiple loads and complex power consumption behaviors. It has high robustness and engineering feasibility, and is suitable for applications such as distribution side monitoring, electric vehicle charging management, and power safety analysis.
[0052] Example 2 like Figure 3 As shown, based on the same inventive concept as the above embodiments, the present invention also provides an electric vehicle charging identification device based on overshoot detection and current characteristic analysis, comprising: The data acquisition module is used to acquire real-time current data of the power circuit, perform reference current correction on the real-time current data to form a test current sequence updated in time order, and initialize the detection start position of overshoot detection. The overshoot detection module is used to perform overshoot detection from the detection start position in the current test current sequence. When a current overshoot that meets the preset overshoot judgment condition is detected, the current overshoot and the corresponding current change range are determined as the current power consumption event. The event classification module is used to classify the current power consumption event into a short-time overshoot event or a long-time overshoot event based on the duration of the current change range corresponding to the current power consumption event. The short-time overshoot module is used to determine the power consumption segment corresponding to the short-time overshoot event in the test current sequence if the current power consumption event is a short-time overshoot event. It then calculates the similarity between this power consumption segment and multiple pre-configured complete current waveforms of household appliances. When there is a complete current waveform of a household appliance with a similarity greater than a preset similarity threshold, the module subtracts the current component corresponding to the complete current waveform of the household appliance from the test current sequence to obtain an updated test current sequence. The module then updates the detection start position to the position after the current power consumption event, updates the updated test current sequence to the current test current sequence, and returns to execute the overshoot detection. The long-term overshoot module is used to perform power consumption stability feature analysis on the test current sequence if the current power consumption event is a long-term overshoot event. When the power consumption stability feature meets the preset stability condition in terms of duration, it determines that there is electric vehicle charging behavior and outputs the corresponding identification result. When the power consumption stability feature does not meet the preset stability condition in terms of duration, it updates the detection start position to the position after the current power consumption event and returns to execute overshoot detection.
[0053] Example 3 like Figure 4 As shown, the present invention also provides an electronic device 100 for implementing an electric vehicle charging identification method based on overshoot detection and current characteristic analysis; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.
[0054] The memory 101 can be used to store the computer program 103. The processor 102 implements the electric vehicle charging identification method based on overcharge detection and current characteristic analysis in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.
[0055] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.
[0056] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.
[0057] The memory 101 in the electronic device 100 stores multiple instructions to implement an electric vehicle charging identification method based on overshoot detection and current characteristic analysis. The processor 102 can execute multiple instructions to achieve the following: Acquire real-time current data of the power circuit, and perform reference current correction on the real-time current data to form a test current sequence updated in time order, and initialize the detection start position of overshoot detection. In the current test current sequence, overshoot detection is performed starting from the detection start position. When a current overshoot that meets the preset overshoot judgment condition is detected, the current overshoot and the corresponding current change range are determined as the current power consumption event. Based on the duration of the current change range corresponding to the current power consumption event, the current power consumption event is classified into a short-term overshoot event or a long-term overshoot event. If the current power consumption event is a short-term overshoot event, the power consumption segment corresponding to the short-term overshoot event is determined in the test current sequence, and the similarity of the power consumption segment with the complete current waveforms of multiple pre-configured household appliances is calculated; when there is a complete current waveform of a household appliance with a similarity greater than a preset similarity threshold, the current component corresponding to the complete current waveform of the household appliance is subtracted from the test current sequence to obtain an updated test current sequence; the detection start position is updated to the position after the current power consumption event, and the updated test current sequence is updated to the current test current sequence, and the overshoot detection is returned to be executed; If the current power consumption event is a long-duration overshoot event, power consumption stability feature analysis is performed on the test current sequence; when the power consumption stability feature meets the preset stability condition in terms of duration, it is determined that there is electric vehicle charging behavior and the corresponding identification result is output; when the power consumption stability feature does not meet the preset stability condition in terms of duration, the detection start position is updated to the position after the current power consumption event, and the overshoot detection is returned to be executed.
[0058] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).
[0059] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0060] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0063] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A method for electric vehicle charging identification based on overshoot detection and current signature analysis, characterized in that, The method comprises: obtaining real-time current data of a power consumption circuit, and performing reference current correction on the real-time current data to form a test current sequence updated in time sequence, and initializing a detection start position of overcurrent detection; performing overcurrent detection from the detection start position in the current test current sequence, and determining a current overcurrent and a corresponding current change interval as a current power consumption event when a preset overcurrent judgment condition is met; distinguishing the current power consumption event as a short-time overcurrent event or a long-time overcurrent event according to a duration of the current change interval corresponding to the current power consumption event; if the current power consumption event is a short-time overcurrent event, determining a power consumption segment corresponding to the short-time overcurrent event in the test current sequence, and performing similarity calculation on the power consumption segment and a plurality of preconfigured complete current waveforms of household appliances; when there is a complete current waveform of a household appliance with a similarity greater than a preset similarity threshold, removing a current component corresponding to the complete current waveform of the household appliance from the test current sequence to obtain an updated test current sequence; updating the detection start position to a position after the current power consumption event, updating the updated test current sequence as the current test current sequence, and returning to perform the overcurrent detection; if the current power consumption event is a long-time overcurrent event, performing power consumption stability feature analysis on the test current sequence; when the power consumption stability feature meets a preset stability condition in duration, determining that there is an electric vehicle charging behavior and outputting a corresponding recognition result; when the power consumption stability feature does not meet the preset stability condition in duration, updating the detection start position to a position after the current power consumption event, and returning to perform the overcurrent detection.
2. The method of claim 1, wherein the method is based on over-charge detection and current signature analysis. The step of reference current correction comprises: obtaining real-time current data of a power consumption circuit, and performing reference current correction on the real-time current data to form a test current sequence updated in time sequence, and initializing a detection start position of overcurrent detection; performing overcurrent detection from the detection start position in the current test current sequence, and determining a current overcurrent and a corresponding current change interval as a current power consumption event when a preset overcurrent judgment condition is met; distinguishing the current power consumption event as a short-time overcurrent event or a long-time overcurrent event according to a duration of the current change interval corresponding to the current power consumption event; if the current power consumption event is a short-time overcurrent event, determining a power consumption segment corresponding to the short-time overcurrent event in the test current sequence, and performing similarity calculation on the power consumption segment and a plurality of preconfigured complete current waveforms of household appliances; when there is a complete current waveform of a household appliance with a similarity greater than a preset similarity threshold, removing a current component corresponding to the complete current waveform of the household appliance from the test current sequence to obtain an updated test current sequence; 3.The electric vehicle charging identification method based on overshoot detection and current feature analysis of claim 1, wherein, updating the detection start position to a position after the current power consumption event, updating the updated test current sequence as the current test current sequence, and returning to perform the overcurrent detection; if the current power consumption event is a long-time overcurrent event, performing power consumption stability feature analysis on the test current sequence; when the power consumption stability feature meets a preset stability condition in duration, determining that there is an electric vehicle charging behavior and outputting a corresponding recognition result; when the power consumption stability feature does not meet the preset stability condition in duration, updating the detection start position to a position after the current power consumption event, and returning to perform the overcurrent detection. The step of reference current correction comprises:
4. The method for electric vehicle charging identification based on overshoot detection and current signature analysis according to claim 1, characterized in that, obtaining real-time current data of a power consumption circuit, and performing reference current correction on the real-time current data to form a test current sequence updated in time sequence, and initializing a detection start position of overcurrent detection; performing overcurrent detection from the detection start position in the current test current sequence, and determining a current overcurrent and a corresponding current change interval as a current power consumption event when a preset overcurrent judgment condition is met; distinguishing the current power consumption event as a short-time overcurrent event or a long-time overcurrent event according to a duration of the current change interval corresponding to the current power consumption event; if the current power consumption event is a short-time overcurrent event, determining a power consumption segment corresponding to the short-time overcurrent event in the test current sequence, and performing similarity calculation on the power consumption segment and a plurality of preconfigured complete current waveforms of household appliances; when there is a complete current waveform of a household appliance with a similarity greater than a preset similarity threshold, removing a current component corresponding to the complete current waveform of the household appliance from the test current sequence to obtain an updated test current sequence; updating the detection start position to a position after the current power consumption event, updating the updated test current sequence as the current test current sequence, and returning to perform the overcurrent detection; if the current power consumption event is a long-time overcurrent event, performing power consumption stability feature analysis on the test current sequence; when the power consumption stability feature meets a preset stability condition in duration, determining that there is an electric vehicle charging behavior and outputting a corresponding recognition result; when the power consumption stability feature does not meet the preset stability condition in duration, updating the detection start position to a position after the current power consumption event, and returning to perform the overcurrent detection. The step of reference current correction comprises: obtaining real-time current data of a power consumption circuit, and performing reference current correction on the real-time current data to form a test current sequence updated in time sequence, and initializing a detection start position of overcurrent detection; performing overcurrent detection from the detection start position in the current test current sequence, and determining a current overcurrent and a corresponding current change interval as a current power consumption event when a preset overcurrent judgment condition is met; distinguishing the current power consumption event as a short-time overcurrent event or a long-time overcurrent event according to a duration of the current change interval corresponding to the current power consumption event; if the current power consumption event is a short-time overcurrent event, determining a power consumption segment corresponding to the short-time overcurrent event in the test current sequence, and performing similarity calculation on the power consumption segment and a plurality of preconfigured complete current waveforms of household appliances; when there is a complete current waveform of a household appliance with a similarity greater than a preset similarity threshold, removing a current component corresponding to the complete current waveform of the household appliance from the test current sequence to obtain an updated test current sequence; updating the detection start position to a position after the current power consumption event, updating the updated test current sequence as the current test current sequence, and returning to perform the overcurrent detection; if the current power consumption event is a long-time overcurrent event, performing power consumption stability feature analysis on the test current sequence; when the power consumption stability feature meets a preset stability condition in duration, determining that there is an electric vehicle charging behavior and outputting a corresponding recognition result; when the power consumption stability feature does not meet the preset stability condition in duration, updating the detection start position to a position after the current power consumption event, and returning to perform the overcurrent detection. constructing a corresponding waveform feature representation based on the current waveform corresponding to the power consumption segment and a preconfigured complete current waveform of a household appliance; performing similarity calculation on the waveform feature representation of the power consumption segment and the waveform feature representation of each complete current waveform of the household appliance to obtain a similarity value between the power consumption segment and each complete current waveform of the household appliance; comparing the similarity value with a preset similarity threshold value, and determining that the power consumption segment matches the corresponding complete current waveform of the household appliance when the similarity value is greater than the preset similarity threshold value.
5. The method for electric vehicle charging identification based on overshoot detection and current signature analysis according to claim 1, characterized in that, The step of deducting the current component from the test current sequence comprises: aligning the test current sequence with the complete current waveform of the household appliance based on the overshoot start position corresponding to the short-time overshoot event as the alignment starting point; when the sampling frequencies of the test current sequence and the complete current waveform of the household appliance are inconsistent, calculating the current value corresponding to the complete current waveform of the household appliance at the sampling time of the test current sequence based on interpolation or fitting; subtracting the current value of the test current sequence from the corresponding current value of the complete current waveform of the household appliance at the corresponding sampling point after time alignment to obtain an updated test current sequence.
6. The method for electric vehicle charging identification based on overshoot detection and current signature analysis according to claim 1, characterized in that, The step of analyzing the power consumption stability feature comprises: performing discrete frequency domain analysis on the current signal corresponding to the test current sequence to obtain a discrete frequency spectrum of the current signal; calculating a low-frequency power value based on the discrete frequency spectrum within a discrete frequency index interval corresponding to a preset low-frequency frequency range, wherein the low-frequency power value is the sum of the squares of the amplitudes of the frequency components within the discrete frequency index interval; determining that the power consumption behavior corresponding to the test current sequence is an electric vehicle charging behavior when the low-frequency power value satisfies a preset stability threshold condition within a preset duration.
7. The method for electric vehicle charging identification based on overshoot detection and current signature analysis according to claim 1, characterized in that, The complete current waveform of the household appliance is household appliance power consumption waveform data pre-acquired and stored in a waveform database, and the complete current waveform of the household appliance at least includes the current waveform of the household appliance in the startup stage, the stable running stage or the typical working period; The complete current waveform of the household appliance in the waveform database is updated by at least one of the following ways: collecting the power circuit current corresponding to the household appliance during actual operation, and updating the collected current waveform after periodic statistics and normalization processing; or updating the complete current waveform of the household appliance in the waveform database in a remote issuance manner.
8. An electric vehicle charging identification device based on overshoot detection and current signature analysis, characterized in that, comprises: a data acquisition module configured to acquire real-time current data of a power circuit, and perform baseline current correction on the real-time current data to form a test current sequence updated in chronological order and initialize a detection start position of overshoot detection; an overshoot detection module configured to perform overshoot detection from the detection start position in the current test current sequence, and determine a current overshoot and a corresponding current change interval as a current power consumption event when a current overshoot satisfying a preset overshoot determination condition is detected; The event classification module is configured to distinguish the current power consumption event as a short-time overshoot event or a long-time overshoot event according to a duration of a current variation interval corresponding to the current power consumption event. The short-time overshoot module is configured to, if the current power consumption event is a short-time overshoot event, determine a power consumption segment corresponding to the short-time overshoot event in the test current sequence, and perform similarity calculation on the power consumption segment and preconfigured complete current waveforms of household appliances; when there is a complete current waveform of a household appliance with a similarity greater than a preset similarity threshold, a current component corresponding to the complete current waveform of the household appliance is deducted from the test current sequence to obtain an updated test current sequence. The detection start position is updated to a position after the current power consumption event, and the updated test current sequence is updated as a current test current sequence, and the overshoot detection is performed again. The long-time overshoot module is configured to, if the current power consumption event is a long-time overshoot event, perform power consumption stability feature analysis on the test current sequence. When the power consumption stability feature meets a preset stability condition in duration, it is determined that there is an electric vehicle charging behavior and a corresponding recognition result is output; when the power consumption stability feature does not meet the preset stability condition in duration, the detection start position is updated to a position after the current power consumption event, and the overshoot detection is performed again.
9. An electronic device, comprising: The computer readable storage medium stores at least one instruction, and the at least one instruction is executed by the processor to implement the electric vehicle charging recognition method based on overshoot detection and current feature analysis.
10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores at least one instruction, and the at least one instruction is executed by the processor to implement the electric vehicle charging recognition method based on overshoot detection and current feature analysis.